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首页> 外文期刊>The journal of asthma >Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects.
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Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects.

机译:阻塞通风患者及健康受试者验证自动喘息检测。

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摘要

Computerized lung-sound analysis is a sensitive and quantitative method to identify wheezing by its typical pattern on spectral analysis. We evaluated the accuracy of the VRI, a multi-sensor, computer-based device with an automated technique of wheeze detection. The method was validated in 100 sound files from seven subjects with asthma or chronic obstructive pulmonary disease and seven healthy subjects by comparison of auscultation findings, examination of audio files, and computer detection of wheezes. Three blinded physicians identified 40 sound files with wheezes and 60 sound files without wheezes. Sensitivity and specificity were 83% and 85%, respectively. Negative predictive value and positive predictive value were 89% and 79%, respectively. Overall inter-rater agreement was 84%. False positive cases were found to contain sounds that simulate wheezes, such as background noises with high frequencies or strong noises from the throat that could be heard and identified without a stethoscope. The present findings demonstrate that the wheeze detection algorithm has good accuracy, sensitivity, specificity, negative predictive value and positive predictive value for wheeze detection in regional analyses with a single sensor and multiple sensors. Results are similar to those reported in the literature. The device is user-friendly, requires minimal patient effort, and, distinct from other devices, it provides a dynamic image of breath sound distribution with wheeze detection output in less than 1 minute.
机译:计算机化肺部声音分析是一种敏感和定量的方法,以通过其典型模式识别速度在光谱分析中。我们评估了VRI,多传感器,基于计算机的装置的准确性,具有喘息检测的自动化技术。通过比较听诊调查结果,音频文件检查和计算机检测,从哮喘或慢性阻塞性肺病和七个健康受试者的七项受试者中验证了该方法。三个盲盲的医师用喘息的喘息和60个声音文件确定了40个声音文件,没有喘息。敏感性和特异性分别为83%和85%。负预测值和阳性预测值分别为89%和79%。整体税率协议为84%。发现假阳性案例包含模拟喘息的声音,例如具有高频率的背景噪声或从喉部的强烈噪音,可以在没有听诊器的情况下识别。本研究结果表明,喘息检测算法具有良好的精度,灵敏度,特异性,负预测值,对具有单个传感器和多个传感器的区域分析中的喘息检测的阳性预测值。结果类似于文献中报告的结果。该设备是用户友好的,需要最小的患者努力,并且与其他设备不同,它提供了呼吸声分布的动态图像,在不到1分钟的速度检测输出。

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